Orthorectification of Sich-2 satellite images using elastic models

Kravchenko, Oleksii; Lavrenyuk, M. V.; Kussul, Nataliia

doi:10.1109/igarss.2014.6946925

Cited by 1 publication

(1 citation statement)

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“…Compared to other area-based methods (e.g. cross-correlation in the spatial domain), the phase-correlation image usually contains a sharp peak corresponding to the dominant shift between images, and is usually more robust to temporal changes between reference and sensed images (Kravchenko et al 2014). Compared to feature-based methods, the phasecorrelation approach with a moving window allows detection of a dense grid of CPs, especially in cases where features cannot be reliably identified and detected, including at the 30 m spatial resolution imagery.…”

Section: Automatic Generation Of Cpsmentioning

confidence: 99%

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

Skakun

Roger

Vermote

et al. 2017

International Journal of Digital Earth

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This study investigates misregistration issues between Landsat-8/OLI and Sentinel-2A/MSI at 30 m resolution, and between multi-temporal Sentinel-2A images at 10 m resolution using a phase correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear Random Forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07±0.02 pixels at 30 m resolution, and 0.09±0.05 and 0.15±0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1 st order polynomial function (affine transformation) yielded RMSE of 0.08±0.02 pixels at 30 m resolution and 0.12±0.06 (same Sentinel-2A orbits) and 0.20±0.09 (adjacent orbits) pixels at 10 m resolution.

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Section: Automatic Generation Of Cpsmentioning

confidence: 99%